Super Resolution. We apply the proposed GCC to compress SRGAN [1]}, which uses a DenseNet-like [2]} generator to upscale low-resolution images.
The training set and validation set of COCO [3]} dataset are combined for the training process.
We randomly crop 96\(\times \) 96 high-resolution images from the original images and then downsample it 4\(\times \)  into low-resolution images as the input of the generator.
For testing, we employ Set5 [4]}, Set14 [1]}, BSD100 [6]}, and Urban100 [7]} as the standard benchmark datasets.
Peak Signal-to-Noise Ratio (PSNR) [8]} and Structural Similarity (SSIM) [9]} are used to evaluate the quality of the generated images.
The higher score indicates the better the image generation performance.
More implementation details are illustrated in Appendix D.
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